Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Understanding the physical mechanisms that govern the formation of molecular complexes is key to controlling the assembly of nanomachines and new materials.
Molecular dynamics simulations and single-molecule experiments offer the unprecedented possibility to reveal mechanisms of molecular self-organization in high resolution. Machine learning and artificial intelligence promise to empower both approaches to overcome fundamental challenges.
In the first part of my talk, I will present an autonomous AI that learns molecular mechanisms from computer simulations. The AI agent simulates infrequent and stochastic molecular reorganizations and progressively learns how to predict their outcome. By using symbolic regression, we distill simplified quantitative models that reveal mechanistic insight in a human-understandable form. Our innovative AI enables the sampling of rare events by autonomously driving many parallel simulations with minimal human intervention and aids their mechanistic interpretation. I will present applications on nucleation processes and the assembly of membrane proteins in lipid bilayers.
In the second part of my talk, I will discuss how the integration of physical modeling and AI helps extracting mechanistic understanding from single-molecule force spectroscopy. While these experiments offer the possibility of measuring fundamental quantities like free energies, these measurements are often incomplete and indirect. In practice, we measure a few order parameters that are the outcome of the coupled dynamics of the molecule and the mesoscopic experimental apparatus, which could lead to estimation artifacts. I will discuss this problem as a Bayesian inference and illustrate how simulation-based inference provides a powerful solution. Coupling a simulator that encodes the physics of the measuring process with density estimation using neural networks leads to accurate estimates of molecular free energies.
In conclusion, the integration of physics-based models and AI provides a general and powerful way to extract accurate quantitative information from simulations and biophysical experiments.